Dataset Card for "ms_marco"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://microsoft.github.io/msmarco/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 1481.03 MB
- Size of the generated dataset: 4503.32 MB
- Total amount of disk used: 5984.34 MB
Dataset Summary
Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search.
There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions
This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1).
The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below.
The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker.
version v1.1
Supported Tasks
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
v1.1
- Size of downloaded dataset files: 160.88 MB
- Size of the generated dataset: 414.48 MB
- Total amount of disk used: 575.36 MB
An example of 'train' looks as follows.
v2.1
- Size of downloaded dataset files: 1320.14 MB
- Size of the generated dataset: 4088.84 MB
- Total amount of disk used: 5408.98 MB
An example of 'validation' looks as follows.
Data Fields
The data fields are the same among all splits.
v1.1
answers
: alist
ofstring
features.passages
: a dictionary feature containing:is_selected
: aint32
feature.passage_text
: astring
feature.url
: astring
feature.
query
: astring
feature.query_id
: aint32
feature.query_type
: astring
feature.wellFormedAnswers
: alist
ofstring
features.
v2.1
answers
: alist
ofstring
features.passages
: a dictionary feature containing:is_selected
: aint32
feature.passage_text
: astring
feature.url
: astring
feature.
query
: astring
feature.query_id
: aint32
feature.query_type
: astring
feature.wellFormedAnswers
: alist
ofstring
features.
Data Splits Sample Size
name | train | validation | test |
---|---|---|---|
v1.1 | 82326 | 10047 | 9650 |
v2.1 | 808731 | 101093 | 101092 |
Dataset Creation
Curation Rationale
Source Data
Annotations
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{DBLP:journals/corr/NguyenRSGTMD16,
author = {Tri Nguyen and
Mir Rosenberg and
Xia Song and
Jianfeng Gao and
Saurabh Tiwary and
Rangan Majumder and
Li Deng},
title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
journal = {CoRR},
volume = {abs/1611.09268},
year = {2016},
url = {http://arxiv.org/abs/1611.09268},
archivePrefix = {arXiv},
eprint = {1611.09268},
timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
}
Contributions
Thanks to @mariamabarham, @thomwolf, @lewtun for adding this dataset.